In the proposal presentation we had mentioned that all the code would be written on AWS. However, I realised that Google Colab is better for development. The reason for this is that Colab gives us free access to a GPU. This allows us to spend a potentially unbounded amount of time developing the model without constraining on our budget. When we are ready to deploy the code, we can then export it to AWS, because that is more robust.
This week, I set up the pipeline to train the ML model for the project. Usually this is done when the dataset is available, so that the data can be preprocessed and tested. Since in this case the data is not yet available, I built the pipeline to the best of my ability without it.
I also spent a good amount of time researching tools for when the data is available. One of the tools required is an annotation tool. This will allow me to draw bounding boxes over the data images and set labels using a GUI. Since I there are 10k images we hope to get, an annotation tool can significantly speed up the labelling process which could be a bottleneck going forward. In my research, I found LabelImg (https://github.com/tzutalin/labelImg) which seems to be the best annotation software because it is compatible with the PASCAL VOC format that is required by YOLO.
With reference to the machine learning, we are on schedule. I can start working on the next big steps once I have access to the data.
References:
Pipeline:
https://medium.com/oracledevs/final-layers-and-loss-functions-of-single-stage-detectors-part-1-4abbfa9aa71c
https://www.curiousily.com/posts/object-detection-on-custom-dataset-with-yolo-v5-using-pytorch-and-python/
Annotations:
https://github.com/tzutalin/labelImg
https://github.com/ujsyehao/COCO-annotations-darknet-format
https://github.com/wkentaro/labelme